compcomp: Data structured for glm approach in competing risk setting
In cribosch/matchsurv: Excess Risk Estimation for Matched Cohort Survival Data

Description Usage Arguments Value Author(s) Examples

Data structured for glm approach in competing risk setting

1 2	compcomp(formula, data, cluster, idControl, strata = NULL, time.points, cens.formula = NULL, cens.code = 0, event = 1)

`formula`	formula with 'Event' outcome (see `timereg` package); time stands for the start time, while time2 stands for the stop time. cause=1 will be considered as the event of interest
`data`	data frame
`cluster`	vector cluster indicator (one cluster for each exposed individual)
`idControl`	vector control indicator (idControl==1 indicates exposed individual in cluster i)
`strata`	weights computed according to strata
`time.points`	vector of time points where the glm will be estimated (10 usually is a sufficient number; the more time points, the slower the glm function)
`cens.formula`	useful to estimate the weights when censoring is present. no quotes, add something like ~age+year
`cens.code`	default is 0
`event`	in which event are you interested in?

A setup dataset, ready for geese

Cristina Boschini

dcif<-sim.data.MatchCR(nca=1000, ncont=5)
tp<-c(0.5,1,2,5,10,15,25)
setdcif1<-compcomp(timereg::Event(time=FALSE,time2=time,cause=cause)~X1+X2,
 data=dcif, cluster=i, idControl=j, time.points=tp, cens.formula=NULL, event=1)
head(setdcif1, 10)
setdcif2<-compcomp(timereg::Event(time=FALSE,time2=time,cause=cause)~X1+X2,
 data=dcif, cluster=i, idControl=j, time.points=tp, cens.formula=NULL, event=2)
head(setdcif2, 10)